Enhancing Energy Demand Predictability through Dynamic LoRa  Network Topologies and Feature-Optimized Machine Learning

Komati Sathish

Enhancing Energy Demand Predictability through Dynamic LoRa Network Topologies and Feature-Optimized Machine Learning

Authors

Komati Sathish

Keywords:

LoRa technology, Mutli-hop wireless communication, Internal of things (IoT), Energy demand forecasting, Dynamic routing, Random forest classifier, Support vector classifier.

Abstract

This research presents a Long Range (LoRa)-based multi-hop wireless communication architecturedesigned to enhance IoT sensor data collection for accurate energy demand forecasting. Byimplementing a multi-hop paradigm and dynamic routing, the system overcomes the geographic limitations of traditional LoRa technology

References

. Dafflon, B.; Wielandt, S.; Lamb, J.; McClure, P.; Shirley, I.; Uhlemann, S.; Wang, C.; Fiolleau, S.; Brunetti, C.; Akins, F.H.; et al. A distributed temperature profiling system for vertically and laterally dense acquisition of soil and snow temperature. Cryosphere 2022, 16, 719–736.

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Published

2023-07-15

How to Cite

Komati Sathish. (2023). Enhancing Energy Demand Predictability through Dynamic LoRa Network Topologies and Feature-Optimized Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2548–2558. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4937